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The impact of graphite nanoplatelets (GNPs) on the physical and mechanical properties of cementitious nanocomposites was investigated. A market-available premixed mortar was modified with 0.01% by weight of cement of commercial GNPs characterized by two distinctively different aspect ratios.The rheological behavior of the GNP-modified fresh admixtures was thoroughly evaluated. Hardened cementitious nanocomposites were investigated in terms of density, microstructure (Scanning Electron Microscopy, SEM and micro–Computed Tomography, μ-CT), mechanical properties (three-point bending and compression tests), and physical properties (electrochemical impedance spectroscopy, EIS and thermal conductivity measurements). At 28 days, all GNP-modified mortars showed about 12% increased density. Mortars reinforced with high aspect ratio GNPs exhibited the highest compressive and flexural strength: about 14% and 4% improvements compared to control sample, respectively. Conversely, low aspect ratio GNPs led to cementitious nanocomposites characterized by 36% decreased electrical resistivity combined with 60% increased thermal conductivity with respect to the control sample.  相似文献   
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Notch pathway is a highly conserved intracellular signaling route that modulates a vast variety of cellular processes including proliferation, differentiation, migration, cell fate and death. Recently, the presence of a strict crosstalk between Notch signaling and inflammation has been described, although the precise molecular mechanisms underlying this interplay have not yet been fully unravelled. Disruptions in Notch cascade, due both to direct mutations and/or to an altered regulation in the core components of Notch signaling, might lead to hypo- or hyperactivation of Notch target genes and signaling molecules, ultimately contributing to the onset of autoinflammatory diseases. To date, alterations in Notch signaling have been reported as associated with three autoinflammatory disorders, therefore, suggesting a possible role of Notch in the pathogenesis of the following diseases: hidradenitis suppurativa (HS), Behçet disease (BD), and giant cell arteritis (GCA). In this review, we aim at better characterizing the interplay between Notch and autoinflammatory diseases, trying to identify the role of this signaling route in the context of these disorders.  相似文献   
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Durum wheat (Triticum durum Desf.) has potential as an adjunct in brewing given its agronomic, chemical and technological properties. The aim of this work were to identify the cultivable microflora and evaluate the technological quality of the durum wheat variety ‘Senatore Cappelli’ grown and used by a craft brewery in Sardinia, Italy. The isolated bacterial strains were mainly rhizospheric (Kocuria rizophila, Microbacterium aerolatum and Bacillus pumilus) and associated with the microbiota of wheat (Staphylococcus spp.). None have been reported previously as spoilage species in brewing. The dominant yeast genera were Cryptococcus spp. and Rhodotorula spp., followed by Saccharomyces cerevisiae. The dominant filamentous fungus genera were Alternaria and Rhizopus. Low levels of mycotoxigenic Fusarium spp., Aspergillus spp. and Penicillium spp. were isolated. However, the levels of deoxynivalenol, T2‐HT2, fumonisin, aflatoxin and ochratoxin detected in the malt and grain were below the thresholds defined by European law. Malt obtained from raw grain showed interesting technological properties, but required specific malting parameters different from those of common wheat and barley. These data suggest that the use of locally grown durum wheat in brewing can increase sustainability and reduce costs, while reinforcing the link with the terroir and promoting reduced mycotoxin levels. © 2019 The Institute of Brewing & Distilling  相似文献   
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We present a simple and effective technique for absolute colorimetric camera characterization, invariant to changes in exposure/aperture and scene irradiance, suitable in a wide range of applications including image‐based reflectance measurements, spectral pre‐filtering and spectral upsampling for rendering, to improve colour accuracy in high dynamic range imaging. Our method requires a limited number of acquisitions, an off‐the‐shelf target and a commonly available projector, used as a controllable light source, other than the reflected radiance to be known. The characterized camera can be effectively used as a 2D tele‐colorimeter, providing the user with an accurate estimate of the distribution of luminance and chromaticity in a scene, without requiring explicit knowledge of the incident lighting power spectra. We validate the approach by comparing our estimated absolute tristimulus values (XYZ data in ) with the measurements of a professional 2D tele‐colorimeter, for a set of scenes with complex geometry, spatially varying reflectance and light sources with very different spectral power distribution.  相似文献   
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Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.  相似文献   
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